Bibliographic record
Abstract
On December 17, 1985, Federal Consumer and Corporate Affairs Minister Michel Cote tabled in the House of Commons a bill to amend Canada's present competition law. Although Mr. Cote claimed that the new legislation will protect the marketplace and give consumers the widest selection of goods at the lowest possible this is extremely unlikely. A Combines Investigation Act which interferes with our system of competitive enterprise will instead stifle business rivalry, and lead to economic inefficiency. This, in turn, will reduce the welfare of the Canadian consumer. The main drawback in Mr. Cote's initiative is that it is predicated on an untenable and outmoded economic theory. This is the view that business concentration and rivalrous competition are incompatible. It, in turn, stems from the textbook model of perfect competition, which declares ideal a scenario in which firms are small and numerous, goods are homogeneous and unchanging, information is costless and profits are always zero. But in the real world which Canada for better or worse inhabits, these conditions are irrelevant to rivalrous business struggle. In the marketplace, large scale enterprises, even gigantic ones, are not exempt from competition. They, too, can fail if they cease to satisfy consumer demands for quality, low price, efficiency, new products, good service and reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.041 | 0.030 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".